jihunchoi

def _sequence_mask(sequence_length, max_len=None):
    if max_len is None:
        max_len = sequence_length.data.max()
    batch_size = sequence_length.size(0)
    seq_range = torch.range(0, max_len - 1).long()
    seq_range_expand = seq_range.unsqueeze(0).expand(batch_size, max_len)
    seq_range_expand = Variable(seq_range_expand)
    if sequence_length.is_cuda:
        seq_range_expand = seq_range_expand.cuda()
    seq_length_expand = (sequence_length.unsqueeze(1)

cbaziotis

from keras import backend as K, initializers, regularizers, constraints
from keras.engine.topology import Layer


def dot_product(x, kernel):
    """
    Wrapper for dot product operation, in order to be compatible with both
    Theano and Tensorflow
    Args:

ziadoz

#!/usr/bin/env bash
# https://developers.supportbee.com/blog/setting-up-cucumber-to-run-with-Chrome-on-Linux/
# https://gist.github.com/curtismcmullan/7be1a8c1c841a9d8db2c
# https://stackoverflow.com/questions/10792403/how-do-i-get-chrome-working-with-selenium-using-php-webdriver
# https://stackoverflow.com/questions/26133486/how-to-specify-binary-path-for-remote-chromedriver-in-codeception
# https://stackoverflow.com/questions/40262682/how-to-run-selenium-3-x-with-chrome-driver-through-terminal
# https://askubuntu.com/questions/760085/how-do-you-install-google-chrome-on-ubuntu-16-04

# Versions
CHROME_DRIVER_VERSION=`curl -sS https://chromedriver.storage.googleapis.com/LATEST_RELEASE`

wassname

"""
A keras attention layer that wraps RNN layers.

Based on tensorflows [attention_decoder](https://github.com/tensorflow/tensorflow/blob/c8a45a8e236776bed1d14fd71f3b6755bd63cc58/tensorflow/python/ops/seq2seq.py#L506) 
and [Grammar as a Foreign Language](https://arxiv.org/abs/1412.7449).

date: 20161101
author: wassname
url: https://gist.github.com/wassname/5292f95000e409e239b9dc973295327a
"""

dswah

from keras import backend as K
from keras import activations, initializations, regularizers, constraints
from keras.engine import Layer, InputSpec
from keras.utils.np_utils import conv_output_length
from keras.layers import Convolution1D, Convolution2D
import tensorflow as tf

class Convolution1D_tied(Layer):
    '''Convolution operator for filtering neighborhoods of one-dimensional inputs.
    When using this layer as the first layer in a model,

udibr

import numpy as np
from keras.layers import GRU, initializations, K
from collections import OrderedDict

class GRULN(GRU):
    '''Gated Recurrent Unit with Layer Normalization
    Current impelemtation only works with consume_less = 'gpu' which is already
    set.

    # Arguments

fchollet

import theano
from keras.models import Sequential
from keras.layers.core import Dense, Activation

X_train, y_train = ... # load some training data
X_batch = ... # a batch of test data

# this is your initial model
model = Sequential()
model.add(Dense(20, 64))